EDA-Coursera-wk2
MCCurcio
12/31/2020
EDA-Coursera-wk2
Lattice Plotting System (part 1)
xyplot, bwplot, levelplot, grid graphics, stripplot, dotplot, splom, contourplot, histograms
Lattice requires one to make the plot “all at once.”
Simple xyplot
xyplot(y ~ x | f * g, data)
Example #1
library(lattice)
library(datasets)
xyplot(Ozone ~ Wind, data = airquality)
5 Panel factor xyplot
airquality <- transform(airquality,
Month = factor(Month))
xyplot(Ozone ~ Wind | Month,
data = airquality,
layout = c(5,1))
# Plots May to Sept by factor(month)Lattice Behavior - Lattice returns an object of the class trellis - auto-prints
Saving plots to workspace
p <- xyplot(Ozone ~ Wind, data = airquality) # Nothing prints
print(p) # Now it prints
Lattice Plotting System (part 2)
set.seed(100)
x <- rnorm(100)
f <- rep(0:1, each = 50)
y <- x + f - f * x + rnorm(100, sd = 0.5)
f <- factor(f, labels = c("Grupo1", "Grupo2"))
xyplot(y ~ x | f, layout = c(2,1)) # plot 2 cols, 1 row
Plots w/ median lines
xyplot(y ~ x | f, panel = function(x,y, ...) {
panel.xyplot(x,y, ...) # 1st call the default function for xyplot
panel.abline(h = median(y), lty = 2) # Adds horizontal line at median
})
Plots w/ regression lines
xyplot(y ~ x | f, panel = function(x,y, ...) {
panel.xyplot(x,y, ...) # 1st call the default function for xyplot
panel.lmline(x, y, col = 3) # Adds regression lines
})
Example: See: Mouse Allergen & Asthma Cohort Study Ahluwalia etal, J. Allergy & Clinic. Immun., 2013
Summary:
- Lattice req. one function call
- Margins and Spacing are automatics and default values usually work
- Lattice plots can be good for conditioning plots
ggplot2 part 1
gg = “grammar of graphics” by Leland Wilkinson, written by Hadley W as a graduate stud.
See: http://ggplot2.org for documentation
- the grammar of graphics tells us that we have mapping from data to aesthetic attributes (color, shape, size) of geometry (points, lines, bars) drawn on a coordinate system.
Basic: qplot()
library(ggplot2)
head(mpg)## # A tibble: 6 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compa…
## 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compa…
## 3 audi a4 2 2008 4 manual(m6) f 20 31 p compa…
## 4 audi a4 2 2008 4 auto(av) f 21 30 p compa…
## 5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compa…
## 6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compa…str(mpg)## tibble [234 × 11] (S3: tbl_df/tbl/data.frame)
## $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
## $ model : chr [1:234] "a4" "a4" "a4" "a4" ...
## $ displ : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
## $ drv : chr [1:234] "f" "f" "f" "f" ...
## $ cty : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : chr [1:234] "p" "p" "p" "p" ...
## $ class : chr [1:234] "compact" "compact" "compact" "compact" ...Simple ggplot2 example
qplot(x =displ, y = hwy, data = mpg)
Modifying Aesthetics
qplot(x = displ, y = hwy, data = mpg, color = drv)
Adding a geom (Geometry)
qplot(x = displ, y = hwy, data = mpg, geom = c("point", "smooth"))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Histograms
qplot(hwy, data = mpg, fill = drv, bins = 40)
Facets
qplot(displ, hwy, data = mpg, facets = .~drv)
qplot(hwy, data = mpg, facets = drv ~., binwidth = 2)
Example from R.Peng - Mouse Allergen & Asthma study - Baltimore children (5-17) - Study indoor enviro and its relationship w asthma morbidity - See: http://goo.gl/Wqe9j8 - Partial data currently available publically
“simulated” maasc dataset
setwd("~/Dropbox/R_exercises/Coursera/Exploratory Data Analysis/week2")
load("maacs.Rda")
str(maacs)## 'data.frame': 750 obs. of 9 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ eno : num 141 124 126 164 99 68 41 50 12 30 ...
## $ duBedMusM : num 2423 2793 3055 775 1634 ...
## $ pm25 : num 15.6 34.4 39 33.2 27.1 ...
## $ mopos : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ logpm25 : num 1.19 1.54 1.59 1.52 1.43 ...
## $ NocturnalSympt: int 0 0 2 2 2 2 0 1 0 0 ...
## $ bmicat : Factor w/ 2 levels "normal weight",..: 1 2 2 1 1 1 2 2 2 1 ...
## $ logno2_new : num 1.62 1.88 1.71 1.46 1.29 ...qplot(log(eno), data = maacs)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 108 rows containing non-finite values (stat_bin).
qplot(log(eno), data = maacs, fill = mopos)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 108 rows containing non-finite values (stat_bin).
Density Smooth Plots
qplot(log(eno), data = maacs, geom = "density")
Density Smooth Plots w/ Color
qplot(log(eno), data = maacs, geom = "density", color = mopos)
Scatter plot1: eNO vs. PM25
qplot(log(pm25), log(eno), data = maacs)
Scatter plot2: eNO vs. PM25
qplot(log(pm25), log(eno), data = maacs, shape = mopos)
Scatter plot3: eNO vs. PM25
qplot(log(pm25), log(eno), data = maacs, color = mopos)## Warning: Removed 184 rows containing missing values (geom_point).
Scatter plots w Smoothing and Linear Modeling
qplot(log(pm25), log(eno), data = maacs, color = mopos) +
geom_smooth(method = "lm")
Splitting data with facets
qplot(log(pm25),
log(eno),
data = maacs,
color = mopos,
facets = . ~ mopos) +
geom_smooth(method = "lm")
NOTE: For ggplot2; It is difficult to go against the grain or customize(so don’t bother) OR learn/use full ggplo2 methods with more complicated methods.
ggplot2 (part 3)
Fundamentals only - Plots are built up in layers - data frame - aesthetic mappings: color, size - geoms: points, lines and shapes - facets: for conditional plots - stats: transformations, i.e. binning, quantiles, smoothing - scales: factors like m/f - coordinate system
Basic plotting
qplot(log(pm25),
NocturnalSympt,
data = maacs,
facets = .~bmicat,
geom = c("point", "smooth"),
method = "lm")
Building up layers
head(maacs[1:3,1:3])## id eno duBedMusM
## 1 1 141 2423
## 2 2 124 2793
## 3 3 126 3055g <- ggplot(maacs, aes(log(pm25), NocturnalSympt))
print(g)
summary(g)## data: id, eno, duBedMusM, pm25, mopos, logpm25, NocturnalSympt, bmicat,
## logno2_new [750x9]
## mapping: x = ~log(pm25), y = ~NocturnalSympt
## faceting: <ggproto object: Class FacetNull, Facet, gg>
## compute_layout: function
## draw_back: function
## draw_front: function
## draw_labels: function
## draw_panels: function
## finish_data: function
## init_scales: function
## map_data: function
## params: list
## setup_data: function
## setup_params: function
## shrink: TRUE
## train_scales: function
## vars: function
## super: <ggproto object: Class FacetNull, Facet, gg>p <- g + geom_point()
print(p)
OR
g + geom_point()
ggplot2 (part 4)
Adding More Layers: Smoothing
g + geom_point() +
geom_smooth()
g + geom_point() +
geom_smooth(method = "lm")
Adding Facets - Conditional Vars
g + geom_point() +
facet_grid(. ~ bmicat) +
geom_smooth(method = "lm")
Annotations
- xlab(), ylab, labs, ggtitle
- each geom function has its own options too
- For global issues USE: theme()
- `theme(legend.position = “none”)
- Two std themes include
- theme_gray()
- theme_bw()
Modifying Aesthetics (colors)
g + geom_point(color = "lightblue", size = 4, alpha = 0.75) +
geom_smooth(method = "lm")
g + geom_point(aes(color = bmicat), size = 4, alpha = 0.5) +
geom_smooth(method = "lm")
Mod-ing Labels AND Smoothing
g + geom_point(aes(color = bmicat), size = 3, alpha = 0.5) +
labs(title = "MAACS Study") +
labs(x = "log PM25",
y = "Nocturnal Symptons") +
geom_smooth(method = "lm", se = TRUE)
Changing Themes
g + geom_point(aes(color = bmicat), size = 4, alpha = 0.5) +
theme_bw(base_family = "Times") +
geom_smooth(method = "lm")
ggplot2 (part 5)
Notes about the Axis Limits
Base Plot
testdata <- data.frame(x = 1:100, y = rnorm(100))
testdata[50,2] <- 100 # Giant Outlier
plot(testdata$x, testdata$y, type = "l")
GGPlot2
g <- ggplot(testdata, aes(x=x, y=y)) +
geom_line()
print(g)
WRONG Way
This subsets the data to ONLY include data within the limits NOT using the 50,2 datapoint.
g + ylim(-3, 3)
Better Way
This shows point including outlier with lines proceeding outside the chart limits
g + coord_cartesian(ylim = c(-3, 3))
g <- ggplot(mpg,aes(x=displ,y=hwy,color=factor(year)))g + geom_point()+facet_grid(drv~cyl, margins = TRUE)
g + geom_point()+facet_grid(drv~cyl, margins = TRUE)+geom_smooth(method="lm", se=FALSE,size=2, color="black")## `geom_smooth()` using formula 'y ~ x'
g + geom_point()+facet_grid(drv~cyl, margins = TRUE)+geom_smooth(method="lm", se=FALSE,size=2, color="black")+labs(x = "Displacement", y= "Highway Mileage", title = "Swirl Rules!")## `geom_smooth()` using formula 'y ~ x'
An Other Example by R.Peng
Cutting N.O. into RANGES
# Calculate the deciles of the data
cutpoints <- quantile(maacs$logno2_new, seq(0,1, length = 4), na.rm = TRUE)
# Cut the data into deciles
maacs$no_decile <- cut(maacs$logno2_new, cutpoints)
# See the levels of the newly created factor var
levels(maacs$no_decile)## [1] "(-0.629,1.18]" "(1.18,1.44]" "(1.44,2.48]"# Setup ggplot with data frame
g <- ggplot(maacs, aes(log(pm25), NocturnalSympt))
# Adding Layers
g + geom_point(alpha = 1/3) +
facet_wrap(bmicat ~ no_decile, nrow = 2, ncol = 4) +
geom_smooth(method = "lm", se = FALSE, col = 2) +
theme_bw(base_family = "Times", base_size = 12) +
labs(x = "log PM25") +
labs(y = "Nocturnal Symptons") +
labs(title = "MAACS Study")
Practical R Exercises in swirl
Practical R Exercises in swirl Part 2
- Duration: 10 minutes- swirl Lesson 1: Lattice Plotting System
- Duration: ~3 hours
- swirl Lesson 2: Working with Colors
- Duration: ~3 hours
- swirl Lesson 3: GGPlot2 Part1
- Duration: ~3 hours
- swirl Lesson 4: GGPlot2 Part2
- Duration: ~3 hours
- swirl Lesson 5: GGPlot2 Extras
- Duration: ~3 hours
EDA Lesson 6: Lattice Plotting System
library(lattice)
library(ggplot2)Example: xyplot(y ~ x | f * g, data)
head(airquality)## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6xyplot(Ozone ~ Wind, data = airquality)
xyplot(Ozone ~ Wind, data = airquality, col = "red", pch = 8, main = "Big Apple Data")
xyplot(Ozone ~ Wind | as.factor(Month), data = airquality, layout = c(5,1))
xyplot(Ozone ~ Wind | Month, data = airquality, layout = c(5,1))
p <- xyplot(Ozone~Wind,data=airquality)
print(p)
names(p) # 45 named properties## [1] "formula" "as.table" "aspect.fill"
## [4] "legend" "panel" "page"
## [7] "layout" "skip" "strip"
## [10] "strip.left" "xscale.components" "yscale.components"
## [13] "axis" "xlab" "ylab"
## [16] "xlab.default" "ylab.default" "xlab.top"
## [19] "ylab.right" "main" "sub"
## [22] "x.between" "y.between" "par.settings"
## [25] "plot.args" "lattice.options" "par.strip.text"
## [28] "index.cond" "perm.cond" "condlevels"
## [31] "call" "x.scales" "y.scales"
## [34] "panel.args.common" "panel.args" "packet.sizes"
## [37] "x.limits" "y.limits" "x.used.at"
## [40] "y.used.at" "x.num.limit" "y.num.limit"
## [43] "aspect.ratio" "prepanel.default" "prepanel"p[["formula"]]## Ozone ~ Windp[["x.limits"]]## [1] 0.37 22.03table(f)## f
## Grupo1 Grupo2
## 50 50xyplot(y~x|f, layout = c(2,1))
The panel function has 3 arguments, x, y and … .
p <- xyplot(y ~ x | f, panel = function(x, y, ...) {
panel.xyplot(x, y, ...) ## First call the default panel function for 'xyplot'
panel.abline(h = median(y), lty = 2) ## Add a horizontal line at the median
})
print(p)
invisible()Diamonds
str(diamonds)## tibble [53,940 × 10] (S3: tbl_df/tbl/data.frame)
## $ carat : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...table(diamonds$color)##
## D E F G H I J
## 6775 9797 9542 11292 8304 5422 2808table(diamonds$color, diamonds$cut)##
## Fair Good Very Good Premium Ideal
## D 163 662 1513 1603 2834
## E 224 933 2400 2337 3903
## F 312 909 2164 2331 3826
## G 314 871 2299 2924 4884
## H 303 702 1824 2360 3115
## I 175 522 1204 1428 2093
## J 119 307 678 808 896Review
True or False? Lattice plots are constructed by a series of calls to core functions.
1: True 2: False
Selection: 2
Excellent work!
True or False? Lattice plots are constructed with a single function call to a core lattice function (e.g.
xyplot)
xyplot)
1: True 2: False
Selection: 1
All that hard work is paying off!
True or False? Aspects like margins and spacing are automatically handled and defaults are usually
sufficient.
sufficient.
1: False 2: True
Selection: 2
Perseverance, that’s the answer.
True or False? The lattice system is ideal for creating conditioning plots where you examine the same kind
of plot under many different conditions.
of plot under many different conditions.
1: True 2: False
Selection: 1
You are amazing!
True or False? The lattice system, like the base plotting system, returns a trellis plot object.
1: True 2: False
Selection: 2
That’s a job well done!
True or False? Panel functions can NEVER be customized to modify what is plotted in each of the plot
panels.
panels.
1: False 2: True
Selection: 1
You nailed it! Good job!
True or False? Lattice plots can display at most 20 panels in a single plot.
1: False 2: True
Selection: 1
EDA Lesson 2: Working with Colors
library(jpeg)
library(RColorBrewer)
library(datasets)
colors()## [1] "white" "aliceblue" "antiquewhite"
## [4] "antiquewhite1" "antiquewhite2" "antiquewhite3"
## [7] "antiquewhite4" "aquamarine" "aquamarine1"
## [10] "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2"
## [16] "azure3" "azure4" "beige"
## [19] "bisque" "bisque1" "bisque2"
## [22] "bisque3" "bisque4" "black"
## [25] "blanchedalmond" "blue" "blue1"
## [28] "blue2" "blue3" "blue4"
## [31] "blueviolet" "brown" "brown1"
## [34] "brown2" "brown3" "brown4"
## [37] "burlywood" "burlywood1" "burlywood2"
## [40] "burlywood3" "burlywood4" "cadetblue"
## [43] "cadetblue1" "cadetblue2" "cadetblue3"
## [46] "cadetblue4" "chartreuse" "chartreuse1"
## [49] "chartreuse2" "chartreuse3" "chartreuse4"
## [52] "chocolate" "chocolate1" "chocolate2"
## [55] "chocolate3" "chocolate4" "coral"
## [58] "coral1" "coral2" "coral3"
## [61] "coral4" "cornflowerblue" "cornsilk"
## [64] "cornsilk1" "cornsilk2" "cornsilk3"
## [67] "cornsilk4" "cyan" "cyan1"
## [70] "cyan2" "cyan3" "cyan4"
## [73] "darkblue" "darkcyan" "darkgoldenrod"
## [76] "darkgoldenrod1" "darkgoldenrod2" "darkgoldenrod3"
## [79] "darkgoldenrod4" "darkgray" "darkgreen"
## [82] "darkgrey" "darkkhaki" "darkmagenta"
## [85] "darkolivegreen" "darkolivegreen1" "darkolivegreen2"
## [88] "darkolivegreen3" "darkolivegreen4" "darkorange"
## [91] "darkorange1" "darkorange2" "darkorange3"
## [94] "darkorange4" "darkorchid" "darkorchid1"
## [97] "darkorchid2" "darkorchid3" "darkorchid4"
## [100] "darkred" "darksalmon" "darkseagreen"
## [103] "darkseagreen1" "darkseagreen2" "darkseagreen3"
## [106] "darkseagreen4" "darkslateblue" "darkslategray"
## [109] "darkslategray1" "darkslategray2" "darkslategray3"
## [112] "darkslategray4" "darkslategrey" "darkturquoise"
## [115] "darkviolet" "deeppink" "deeppink1"
## [118] "deeppink2" "deeppink3" "deeppink4"
## [121] "deepskyblue" "deepskyblue1" "deepskyblue2"
## [124] "deepskyblue3" "deepskyblue4" "dimgray"
## [127] "dimgrey" "dodgerblue" "dodgerblue1"
## [130] "dodgerblue2" "dodgerblue3" "dodgerblue4"
## [133] "firebrick" "firebrick1" "firebrick2"
## [136] "firebrick3" "firebrick4" "floralwhite"
## [139] "forestgreen" "gainsboro" "ghostwhite"
## [142] "gold" "gold1" "gold2"
## [145] "gold3" "gold4" "goldenrod"
## [148] "goldenrod1" "goldenrod2" "goldenrod3"
## [151] "goldenrod4" "gray" "gray0"
## [154] "gray1" "gray2" "gray3"
## [157] "gray4" "gray5" "gray6"
## [160] "gray7" "gray8" "gray9"
## [163] "gray10" "gray11" "gray12"
## [166] "gray13" "gray14" "gray15"
## [169] "gray16" "gray17" "gray18"
## [172] "gray19" "gray20" "gray21"
## [175] "gray22" "gray23" "gray24"
## [178] "gray25" "gray26" "gray27"
## [181] "gray28" "gray29" "gray30"
## [184] "gray31" "gray32" "gray33"
## [187] "gray34" "gray35" "gray36"
## [190] "gray37" "gray38" "gray39"
## [193] "gray40" "gray41" "gray42"
## [196] "gray43" "gray44" "gray45"
## [199] "gray46" "gray47" "gray48"
## [202] "gray49" "gray50" "gray51"
## [205] "gray52" "gray53" "gray54"
## [208] "gray55" "gray56" "gray57"
## [211] "gray58" "gray59" "gray60"
## [214] "gray61" "gray62" "gray63"
## [217] "gray64" "gray65" "gray66"
## [220] "gray67" "gray68" "gray69"
## [223] "gray70" "gray71" "gray72"
## [226] "gray73" "gray74" "gray75"
## [229] "gray76" "gray77" "gray78"
## [232] "gray79" "gray80" "gray81"
## [235] "gray82" "gray83" "gray84"
## [238] "gray85" "gray86" "gray87"
## [241] "gray88" "gray89" "gray90"
## [244] "gray91" "gray92" "gray93"
## [247] "gray94" "gray95" "gray96"
## [250] "gray97" "gray98" "gray99"
## [253] "gray100" "green" "green1"
## [256] "green2" "green3" "green4"
## [259] "greenyellow" "grey" "grey0"
## [262] "grey1" "grey2" "grey3"
## [265] "grey4" "grey5" "grey6"
## [268] "grey7" "grey8" "grey9"
## [271] "grey10" "grey11" "grey12"
## [274] "grey13" "grey14" "grey15"
## [277] "grey16" "grey17" "grey18"
## [280] "grey19" "grey20" "grey21"
## [283] "grey22" "grey23" "grey24"
## [286] "grey25" "grey26" "grey27"
## [289] "grey28" "grey29" "grey30"
## [292] "grey31" "grey32" "grey33"
## [295] "grey34" "grey35" "grey36"
## [298] "grey37" "grey38" "grey39"
## [301] "grey40" "grey41" "grey42"
## [304] "grey43" "grey44" "grey45"
## [307] "grey46" "grey47" "grey48"
## [310] "grey49" "grey50" "grey51"
## [313] "grey52" "grey53" "grey54"
## [316] "grey55" "grey56" "grey57"
## [319] "grey58" "grey59" "grey60"
## [322] "grey61" "grey62" "grey63"
## [325] "grey64" "grey65" "grey66"
## [328] "grey67" "grey68" "grey69"
## [331] "grey70" "grey71" "grey72"
## [334] "grey73" "grey74" "grey75"
## [337] "grey76" "grey77" "grey78"
## [340] "grey79" "grey80" "grey81"
## [343] "grey82" "grey83" "grey84"
## [346] "grey85" "grey86" "grey87"
## [349] "grey88" "grey89" "grey90"
## [352] "grey91" "grey92" "grey93"
## [355] "grey94" "grey95" "grey96"
## [358] "grey97" "grey98" "grey99"
## [361] "grey100" "honeydew" "honeydew1"
## [364] "honeydew2" "honeydew3" "honeydew4"
## [367] "hotpink" "hotpink1" "hotpink2"
## [370] "hotpink3" "hotpink4" "indianred"
## [373] "indianred1" "indianred2" "indianred3"
## [376] "indianred4" "ivory" "ivory1"
## [379] "ivory2" "ivory3" "ivory4"
## [382] "khaki" "khaki1" "khaki2"
## [385] "khaki3" "khaki4" "lavender"
## [388] "lavenderblush" "lavenderblush1" "lavenderblush2"
## [391] "lavenderblush3" "lavenderblush4" "lawngreen"
## [394] "lemonchiffon" "lemonchiffon1" "lemonchiffon2"
## [397] "lemonchiffon3" "lemonchiffon4" "lightblue"
## [400] "lightblue1" "lightblue2" "lightblue3"
## [403] "lightblue4" "lightcoral" "lightcyan"
## [406] "lightcyan1" "lightcyan2" "lightcyan3"
## [409] "lightcyan4" "lightgoldenrod" "lightgoldenrod1"
## [412] "lightgoldenrod2" "lightgoldenrod3" "lightgoldenrod4"
## [415] "lightgoldenrodyellow" "lightgray" "lightgreen"
## [418] "lightgrey" "lightpink" "lightpink1"
## [421] "lightpink2" "lightpink3" "lightpink4"
## [424] "lightsalmon" "lightsalmon1" "lightsalmon2"
## [427] "lightsalmon3" "lightsalmon4" "lightseagreen"
## [430] "lightskyblue" "lightskyblue1" "lightskyblue2"
## [433] "lightskyblue3" "lightskyblue4" "lightslateblue"
## [436] "lightslategray" "lightslategrey" "lightsteelblue"
## [439] "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
## [442] "lightsteelblue4" "lightyellow" "lightyellow1"
## [445] "lightyellow2" "lightyellow3" "lightyellow4"
## [448] "limegreen" "linen" "magenta"
## [451] "magenta1" "magenta2" "magenta3"
## [454] "magenta4" "maroon" "maroon1"
## [457] "maroon2" "maroon3" "maroon4"
## [460] "mediumaquamarine" "mediumblue" "mediumorchid"
## [463] "mediumorchid1" "mediumorchid2" "mediumorchid3"
## [466] "mediumorchid4" "mediumpurple" "mediumpurple1"
## [469] "mediumpurple2" "mediumpurple3" "mediumpurple4"
## [472] "mediumseagreen" "mediumslateblue" "mediumspringgreen"
## [475] "mediumturquoise" "mediumvioletred" "midnightblue"
## [478] "mintcream" "mistyrose" "mistyrose1"
## [481] "mistyrose2" "mistyrose3" "mistyrose4"
## [484] "moccasin" "navajowhite" "navajowhite1"
## [487] "navajowhite2" "navajowhite3" "navajowhite4"
## [490] "navy" "navyblue" "oldlace"
## [493] "olivedrab" "olivedrab1" "olivedrab2"
## [496] "olivedrab3" "olivedrab4" "orange"
## [499] "orange1" "orange2" "orange3"
## [502] "orange4" "orangered" "orangered1"
## [505] "orangered2" "orangered3" "orangered4"
## [508] "orchid" "orchid1" "orchid2"
## [511] "orchid3" "orchid4" "palegoldenrod"
## [514] "palegreen" "palegreen1" "palegreen2"
## [517] "palegreen3" "palegreen4" "paleturquoise"
## [520] "paleturquoise1" "paleturquoise2" "paleturquoise3"
## [523] "paleturquoise4" "palevioletred" "palevioletred1"
## [526] "palevioletred2" "palevioletred3" "palevioletred4"
## [529] "papayawhip" "peachpuff" "peachpuff1"
## [532] "peachpuff2" "peachpuff3" "peachpuff4"
## [535] "peru" "pink" "pink1"
## [538] "pink2" "pink3" "pink4"
## [541] "plum" "plum1" "plum2"
## [544] "plum3" "plum4" "powderblue"
## [547] "purple" "purple1" "purple2"
## [550] "purple3" "purple4" "red"
## [553] "red1" "red2" "red3"
## [556] "red4" "rosybrown" "rosybrown1"
## [559] "rosybrown2" "rosybrown3" "rosybrown4"
## [562] "royalblue" "royalblue1" "royalblue2"
## [565] "royalblue3" "royalblue4" "saddlebrown"
## [568] "salmon" "salmon1" "salmon2"
## [571] "salmon3" "salmon4" "sandybrown"
## [574] "seagreen" "seagreen1" "seagreen2"
## [577] "seagreen3" "seagreen4" "seashell"
## [580] "seashell1" "seashell2" "seashell3"
## [583] "seashell4" "sienna" "sienna1"
## [586] "sienna2" "sienna3" "sienna4"
## [589] "skyblue" "skyblue1" "skyblue2"
## [592] "skyblue3" "skyblue4" "slateblue"
## [595] "slateblue1" "slateblue2" "slateblue3"
## [598] "slateblue4" "slategray" "slategray1"
## [601] "slategray2" "slategray3" "slategray4"
## [604] "slategrey" "snow" "snow1"
## [607] "snow2" "snow3" "snow4"
## [610] "springgreen" "springgreen1" "springgreen2"
## [613] "springgreen3" "springgreen4" "steelblue"
## [616] "steelblue1" "steelblue2" "steelblue3"
## [619] "steelblue4" "tan" "tan1"
## [622] "tan2" "tan3" "tan4"
## [625] "thistle" "thistle1" "thistle2"
## [628] "thistle3" "thistle4" "tomato"
## [631] "tomato1" "tomato2" "tomato3"
## [634] "tomato4" "turquoise" "turquoise1"
## [637] "turquoise2" "turquoise3" "turquoise4"
## [640] "violet" "violetred" "violetred1"
## [643] "violetred2" "violetred3" "violetred4"
## [646] "wheat" "wheat1" "wheat2"
## [649] "wheat3" "wheat4" "whitesmoke"
## [652] "yellow" "yellow1" "yellow2"
## [655] "yellow3" "yellow4" "yellowgreen"sample(colors(),10)## [1] "brown2" "darkseagreen2" "beige" "mistyrose4"
## [5] "mediumorchid3" "navajowhite2" "purple4" "royalblue"
## [9] "grey80" "grey85"pal <- colorRamp(c("red","blue"))
pal(0)## [,1] [,2] [,3]
## [1,] 255 0 0pal(1)## [,1] [,2] [,3]
## [1,] 0 0 255pal(seq(0,1,len=6))## [,1] [,2] [,3]
## [1,] 255 0 0
## [2,] 204 0 51
## [3,] 153 0 102
## [4,] 102 0 153
## [5,] 51 0 204
## [6,] 0 0 255p1 <- colorRampPalette(c("red","blue"))
p1(2)## [1] "#FF0000" "#0000FF"p1(6)## [1] "#FF0000" "#CC0033" "#990066" "#650099" "#3200CC" "#0000FF"0xcc## [1] 204#hex 33 to decimal, as in 0x33=3*16+3=51p2 <- colorRampPalette(c("red","yellow"))
p2(2)## [1] "#FF0000" "#FFFF00"p2(10)## [1] "#FF0000" "#FF1C00" "#FF3800" "#FF5500" "#FF7100" "#FF8D00" "#FFAA00"
## [8] "#FFC600" "#FFE200" "#FFFF00"p1## function (n)
## {
## x <- ramp(seq.int(0, 1, length.out = n))
## if (ncol(x) == 4L)
## rgb(x[, 1L], x[, 2L], x[, 3L], x[, 4L], maxColorValue = 255)
## else rgb(x[, 1L], x[, 2L], x[, 3L], maxColorValue = 255)
## }
## <bytecode: 0x55a97d7d5170>
## <environment: 0x55a97d73e340>function (n) { x <- ramp(seq.int(0, 1, length.out = n)) if (ncol(x) == 4L) rgb(x[, 1L], x[, 2L], x[, 3L], x[, 4L], maxColorValue = 255) else rgb(x[, 1L], x[, 2L], x[, 3L], maxColorValue = 255) } <bytecode: 0x559ae13e32d8> <environment: 0x559ae17a2fe0>
rgb {grDevices} - R Documentation - RGB Color Specification
?rgbrgb((0:15)/15, green = 0, blue = 0, names = paste(“red”, 0:15, sep = “.”))
rgb(0, 0:12, 0, max = 255) # integer input
ramp <- colorRamp(c(“red”, “white”)) rgb( ramp(seq(0, 1, length = 5)), max = 255)
p3 <- colorRampPalette(c("blue", "green"), alpha = 0.5)
p3(5)## [1] "#0000FFFF" "#003FBFFF" "#007F7FFF" "#00BF3FFF" "#00FF00FF"# Package ‘RColorBrewer’ loaded correctly!
plot(x, y, pch = 19, col = rgb(0,0.5,0.5))
Alpha = 0.3 (30%)
plot(x, y, pch = 19, col = rgb(0,0.5,0.5, 0.3))
Our last topic for this lesson is the RColorBrewer Package, available on CRAN, that contains interesting and useful color palettes, of which there are 3 types, sequential, divergent, and qualitative. Which one you would choose to use depends on your data.
showMe <- function(cv){
myarg <- deparse(substitute(cv))
z<- outer( 1:20,1:20, "+")
obj<- list( x=1:20,y=1:20,z=z )
image(obj, col=cv, main=myarg )
}cols <- brewer.pal(3, "BuGn")
pal <- colorRampPalette(cols)
showMe(pal(20))
Where: showMe <- function(cv){ myarg <- deparse(substitute(cv)) z<- outer( 1:20,1:20, “+”) obj<- list( x=1:20,y=1:20,z=z ) image(obj, col=cv, main=myarg ) }
image(volcano, col = pal(20))
Which of the following is an R package that provides color palettes for sequential, | categorical, and diverging data?
1: RColorVintner 2: RColorBrewer 3: RColorBluer 4: RColorStewer
Selection: 2
Perseverance, that’s the answer.
True or False? The colorRamp and colorRampPalette functions can be used in conjunction with
color palettes to connect data to colors.
color palettes to connect data to colors.
1: False 2: True
Selection: 2
That’s correct!
True or False? Transparency can NEVER be used to clarify plots with many points
1: True 2: False
Selection: 2
You are doing so well!
True or False? The call p7 <- colorRamp(“red”,“blue”) would work (i.e., not generate an
error).
error).
1: False 2: True
Selection: 2
One more time. You can do it!
Recall our reminders to concatenate the colors to form a single argument.
1: False 2: True
Selection: 1
That’s a job well done!
True or False? The function colors returns only 10 colors.
1: False 2: True
Selection: 1
You’re the best!
Transparency is determined by which parameter of the rgb function?
1: beta 2: delta 3: alpha 4: it’s all Greek to me 5: gamma
Selection: 3
That’s correct!
Lesson 3: GGPlot2 Part1
GGPlot2_Part1. (Slides for this and other Data Science courses may be found at github
See: http://ggplot2.org
str(mpg)## tibble [234 × 11] (S3: tbl_df/tbl/data.frame)
## $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
## $ model : chr [1:234] "a4" "a4" "a4" "a4" ...
## $ displ : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
## $ drv : chr [1:234] "f" "f" "f" "f" ...
## $ cty : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : chr [1:234] "p" "p" "p" "p" ...
## $ class : chr [1:234] "compact" "compact" "compact" "compact" ...qplot(displ, hwy, data=mpg)
qplot(displ, hwy, data=mpg) + aes(color = drv)
# OR
qplot(y=hwy, data = mpg, color = drv)
qplot(displ, hwy, data = mpg, color = drv, geom = c("point", "smooth"))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Box plots with GGplot2
qplot(drv, hwy, data=mpg, geom="boxplot")
Cool
qplot(drv,hwy,data=mpg,geom="boxplot",color=manufacturer)
qplot(hwy, data = mpg, fill = drv)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.qplot(displ, hwy, data=mpg,facets = .~drv)
qplot(hwy, data=mpg,facets = drv~., binwidth = 2)
The facets argument, drv ~ ., resulted in what arrangement of facets?
1: huh? 2: 2 by 2 3: 3 by 1 4: 1 by 3
Selection: 3
Keep working like that and you’ll get there!
Pretty good, right? Not too difficult either. Let’s review what we learned!
…
Which of the following is a basic workhorse function of ggplot2?
1: xyplot 2: hist 3: scatterplot 4: gplot 5: qplot
Selection: 5
Your dedication is inspiring!
Which types of plot does qplot plot?
1: histograms 2: scatterplots 3: all of the others 4: box and whisker plots
Selection: 3
Excellent work!
What does the gg in ggplot2 stand for?
1: grammar of graphics 2: good grief 3: goto graphics 4: good graphics
Selection: 1
Keep working like that and you’ll get there!
True or False? The geom argument takes a string for a value.
1: False 2: True
Selection: 2
You’re the best!
True or False? The data argument takes a string for a value.
1: False 2: True
Selection: 1
Keep up the great work!
True or False? The binwidth argument takes a string for a value.
1: False 2: True
Selection: 1
Nice work!
True or False? The user must specify x- and y-axis labels when using qplot.
1: True 2: False
Selection: 2
GGPlot2 Part2
qplot(displ, hwy, data = mpg, geom=c("point", "smooth"),facets=.~drv)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
g <- ggplot(mpg, aes(displ,hwy) )
g
summary(g)## data: manufacturer, model, displ, year, cyl, trans, drv, cty, hwy, fl,
## class [234x11]
## mapping: x = ~displ, y = ~hwy
## faceting: <ggproto object: Class FacetNull, Facet, gg>
## compute_layout: function
## draw_back: function
## draw_front: function
## draw_labels: function
## draw_panels: function
## finish_data: function
## init_scales: function
## map_data: function
## params: list
## setup_data: function
## setup_params: function
## shrink: TRUE
## train_scales: function
## vars: function
## super: <ggproto object: Class FacetNull, Facet, gg>g+geom_point()
(g + geom_point())+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
g+geom_point()+geom_smooth(method="lm")## `geom_smooth()` using formula 'y ~ x'
g+geom_point()+geom_smooth(method="lm")+facet_grid(.~drv)## `geom_smooth()` using formula 'y ~ x'
g+geom_point(col="pink",size=4,alpha=1/2)
g+geom_point(size=4,alpha=1/2, aes(color=drv))
g+geom_point(aes(color=drv))+labs(title="Swirls Rules!")+labs(x="Displacement", y="Hwy Milage")
g + geom_point(aes(color = drv),size=2,alpha=1/2) +
geom_smooth(size=4,linetype=3,method="lm",se=FALSE)## `geom_smooth()` using formula 'y ~ x'
g + geom_point(aes(color = drv)) + theme_bw(base_family = "serif" )
GGPlot2 Extras
GGPlot2_Extras. (Slides for this and other Data Science courses may be found at github https://github.com/DataScienceSpecialization/courses/. If you care to use them, they must be downloaded as a zip file and viewed locally. This lesson corresponds to 04_ExploratoryAnalysis/ggplot2.)
head(diamonds)## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48qplot(price, data=diamonds)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
qplot(price, data=diamonds, binwidth=18497/30, fill=cut)